Database Access

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially requests and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for retrieving public omics datasets, sequences, annotations, and literature-linked biological resources.

When To Use This Skill

• use when the task is downloading or querying public bioinformatics databases
• use when accessions, identifiers, or search terms must be resolved into data assets
• use when external references such as GEO, SRA, UniProt, Reactome, or PubMed are needed

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• accessions or identifiers
• query terms
• optional species or database constraints

Expected Outputs

• downloaded datasets
• linked metadata tables
• query result summaries

Preferred Tools

• Entrez-style APIs
• UniProt access tools
• Reactome and PubMed resources
• pandas

Starter Pattern

import requests

resp = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", params={
    "db": "gds",
    "term": "single cell liver",
    "retmode": "json",
})
print(resp.text[:500])

Workflow

1. Pick the right database

Choose repositories based on whether the target is raw data, processed data, annotation, pathways, or literature.

2. Query reproducibly

Record identifiers, filters, and database versions or access dates.

3. Normalize returned metadata

Standardize result tables so downstream workflows can join on stable IDs.

4. Download only needed assets

Avoid bulk retrieval when a narrower dataset or accession list solves the task.

5. Export usable references

Save accession tables, metadata joins, and database provenance.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• downloaded datasets
• linked metadata tables
• query result summaries

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Record accession provenance, query parameters, and retrieval date or version.
• Verify that downloaded records map cleanly to the identifiers used downstream.

Anti-Patterns

• mixing identifiers across databases without explicit mapping
• downloading oversized collections when a filtered subset is enough
• citing database content without recording provenance

Related Skills

• Sequence And Format IO
• Alignment And Mapping
• Read QC
• Reporting And Figure Export

Optional Supplements

• pubmed-database
• reactome-database
• string-database